1. Field of the Invention
The present invention generally relates to face annotation techniques, in particular, to a face annotation method and a face annotation system employed within a collaborative framework and a multiple-kernel learning algorithm.
2. Description of Related Art
Over the past decade, the rapid development of digital capturing devices and handheld electronic products has led to a dramatically and continually increasing number of photographs that can be captured at any place and any time. A large proportion of these photographs contain face images which are associated with the daily lives of the photographers who captured them. This has given rise to interest in developing a face recognition system by which to determine the identities of people featured in photographs. Currently, online social networks (OSNs) such as Facebook and MySpace are a prevailing platform on which people communicate with their close friends, family members, and colleagues in the real world. Therefore, research conducted in the area of face recognition has been oriented towards a new service, called face annotation, for the purpose of management and entertainment in regard to social network platforms.
The research field of face annotation techniques can be classified into three approaches: manual, semi-automatic, and automatic. Manual face annotation is the act of annotating identities of individuals or subjects in personal photographs. However, annotation of a large number of uploaded photographs is a time-consuming and labor-intensive task. In effort to improve this situation, semi-automatic face annotation has become a popular approach in the last few years. This technique requires interaction and feedback from users in order to determine the identity label of a given query face in a photograph. While these manual operation procedures are practical for the purposes of face annotation, they still require substantial amounts of user-time. To lessen the need for manual operation and thereby reduce time consumption, the development of automatic face annotation approaches has recently become a prominent research area. This technique automatically determines the identity of subjects in photographs.
To identify faces in personal photographs, the kernel face recognition (FR) engines can be divided into two major systems: single FR classifier and multiple FR classifiers. The single FR classifier system tends to fail at face identification under uncontrolled condition, whereas the multiple FR classifier system is able to achieve practical application in uncontrolled conditions by means of combining a set of single classifiers to obtain accuracy which is superior to that of the single classifier system. Recently, a face annotation approach which uses a collaborative FR framework has been proposed previously. This approach utilizes the three characteristics such as socialization, personalization, and distribution of OSNs to effectively select and merge a set of personalized FR classifiers which belong to members who have close relationships with a particular owner. Nevertheless, an automatic face annotation system which uses a collaborative FR framework is excessively time-consuming because execution time significantly rises when the number of selected FR classifiers increases to the range of 10 to 91.
Accordingly, how to efficiently and accurately achieve highly reliable face annotation results has become an essential topic to researchers for the development of face annotation techniques in OSNs.